Academy of Mathematics and Systems Science, CAS
Colloquia & Seminars

Speaker:	丁亮，复旦大学
Inviter:	熊世峰
Title:	A Sparse Expansion of (Deep) Gaussian Processes
Language:	Chinese
Time & Venue:	2023.03.09 15:00-16:00 思源楼315
Abstract:	Conventional inferential methods for (deep) Gaussian Processes models can suffer from high computational complexity as they require large-scale operations with kernel matrices for training and inference. In this work, we propose an efficient scheme for accurate inference and efficient training based on a range of Gaussian Processes, called the Tensor Markov Gaussian Processes (TMGP). We construct an induced approximation of TMGP referred to as the hierarchical expansion. Next, we develop a deep TMGP (DTMGP) model as the composition of multiple hierarchical expansion of TMGPs. The proposed model has the following properties: (1) the outputs of each activation function are deterministic while the weights are chosen independently from standard Gaussian distribution; (2) in training or prediction, only O(polylog(M)) (out of M) activation functions have non-zero outputs, which significantly boosts the computational efficiency. Our numerical experiments on synthetic models and real dataset.